System and method for providing diagnosis and montoring of congestive heart faliure for use in automated patient care

ABSTRACT

A system and method for providing diagnosis and monitoring of congestive heart failure for use in automated patient care is described. At least one recorded physiological measure is compared to at least one other recorded physiological measure on a substantially regular basis to quantify a change in patient pathophysiological status for equivalent patient information. An absence, an onset, a progression, a regression, and a status quo of congestive heart failure is evaluated dependent upon the change in patient pathophysiological status.

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This patent application is a continuation of U.S. patentapplication Ser. No. 09/441,623, filed Nov. 16, 1999, pending, thedisclosure of which is incorporated herein by reference, and thepriority filing date of which is claimed.

FIELD OF THE INVENTION

[0002] The present invention relates in general to congestive heartfailure (CHF) diagnosis and analysis, and, in particular, to a systemand method for providing diagnosis and monitoring of congestive heartfailure for use in automated patient care throughout disease absence,onset, progression, regression, and status quo.

BACKGROUND OF THE INVENTION

[0003] Presently, congestive heart failure is one of the leading causesof cardiovascular disease-related deaths in the world. Clinically,congestive heart failure involves circulatory congestion caused by heartdisorders that are primarily characterized by abnormalities of leftventricular function and neurohormonal regulation. Congestive heartfailure occurs when these abnormalities cause the heart to fail to pumpblood at a rate required by the metabolizing tissues. The effects ofcongestive heart failure range from impairment during physical exertionto a complete failure of cardiac pumping function at any level ofactivity. Clinical manifestations of congestive heart failure includerespiratory distress, such as shortness of breath and fatigue, andreduced exercise capacity or tolerance.

[0004] Several factors make the early diagnosis and prevention ofcongestive heart failure, as well as the monitoring of the progressionof congestive heart failure, relatively difficult. First, the onset ofcongestive heart failure is generally subtle and erratic. Often, thesymptoms are ignored and the patient compensates by changing his or herdaily activities. As a result, many congestive heart failure conditionsor deteriorations in congestive heart failure remain undiagnosed untilmore serious problems arise, such as pulmonary edema or cardiac arrest.Moreover, the susceptibility to suffer from congestive heart failuredepends upon the patient's age, sex, physical condition, and otherfactors, such as diabetes, lung disease, high blood pressure, and kidneyfunction. No one factor is dispositive. Finally, annual or even monthlycheckups provide, at best, a “snapshot” of patient wellness and theincremental and subtle clinicophysiological changes which portend theonset or progression of congestive heart failure often go unnoticed,even with regular health care. Documentation of subtle improvementsfollowing therapy, that can guide and refine further evaluation andtherapy, can be equally elusive.

[0005] Nevertheless, taking advantage of frequently and regularlymeasured physiological measures, such as recorded manually by a patient,via an external monitoring or therapeutic device, or via implantabledevice technologies, can provide a degree of detection and preventionheretofore unknown. For instance, patients already suffering from someform of treatable heart disease often receive an implantable pulsegenerator (IPG), cardiovascular or heart failure monitor, therapeuticdevice, or similar external wearable device, with which rhythm andstructural problems of the heart can be monitored and treated. Thesetypes of devices are useful for detecting physiological changes inpatient conditions through the retrieval and analysis of telemeteredsignals stored in an on-board, volatile memory. Typically, these devicescan store more than thirty minutes of per heartbeat data recorded on aper heartbeat, binned average basis, or on a derived basis from, forexample, atrial or ventricular electrical activity, minute ventilation,patient activity score, cardiac output score, mixed venous oxygen score,cardiovascular pressure measures, and the like. However, the properanalysis of retrieved telemetered signals requires detailed medicalsubspecialty knowledge, particularly by cardiologists and cardiacelectrophysiologists.

[0006] Alternatively, these telemetered signals can be remotelycollected and analyzed using an automated patient care system. One suchsystem is described in a related, commonly owned U.S. patentapplication, Ser. No. 09/324,894, filed Jun. 3, 1999, pending, thedisclosure of which is incorporated herein by reference. A medicaldevice adapted to be implanted in an individual patient recordstelemetered signals that are then retrieved on a regular, periodic basisusing an interrogator or similar interfacing device. The telemeteredsignals are downloaded via an internetwork onto a network server on aregular, e.g., daily, basis and stored as sets of collected measures ina database along with other patient care records. The information isthen analyzed in an automated fashion and feedback, which includes apatient status indicator, is provided to the patient.

[0007] While such an automated system can serve as a valuable tool inproviding remote patient care, an approach to systematically correlatingand analyzing the raw collected telemetered signals, as well as manuallycollected physiological measures, through applied cardiovascular medicalknowledge to accurately diagnose the onset of a particular medicalcondition, such as congestive heart failure, is needed. One automatedpatient care system directed to a patient-specific monitoring functionis described in U.S. Pat. No. 5,113,869 ('869) to Nappholz et al. The'869 patent discloses an implantable, programmable electrocardiography(ECG) patient monitoring device that senses and analyzes ECG signals todetect ECG and physiological signal characteristics predictive ofmalignant cardiac arrhythmias. The monitoring device can communicate awarning signal to an external device when arrhythmias are predicted.However, the Nappholz device is limited to detecting tachycardias.Unlike requirements for automated congestive heart failure monitoring,the Nappholz device focuses on rudimentary ECG signals indicative ofmalignant cardiac tachycardias, an already well established techniquethat can be readily used with on-board signal detection techniques.Also, the Nappholz device is patient specific only and is unable toautomatically take into consideration a broader patient or peer grouphistory for reference to detect and consider the progression orimprovement of cardiovascular disease. Moreover, the Nappholz device hasa limited capability to automatically self-reference multiple datapoints in time and cannot detect disease regression even in theindividual patient. Also, the Nappholz device must be implanted andcannot function as an external monitor. Finally, the Nappholz device isincapable of tracking the cardiovascular and cardiopulmonaryconsequences of any rhythm disorder.

[0008] Consequently, there is a need for a systematic approach todetecting trends in regularly collected physiological data indicative ofthe onset, progression, regression, or status quo of congestive heartfailure diagnosed and monitored using an automated, remote patient caresystem. The physiological data could be telemetered signals datarecorded either by an external or an implantable medical device or,alternatively, individual measures collected through manual means.Preferably, such an approach would be capable of diagnosing both acuteand chronic congestive heart failure conditions, as well as the symptomsof other cardiovascular diseases. In addition, findings from individual,peer group, and general population patient care records could beintegrated into continuous, on-going monitoring and analysis.

SUMMARY OF THE INVENTION

[0009] The present invention provides a system and method for diagnosingand monitoring the onset, progression, regression, and status quo ofcongestive heart failure using an automated collection and analysispatient care system. Measures of patient cardiovascular information areeither recorded by an external or implantable medical device, such as anIPG, cardiovascular or heart failure monitor, or therapeutic device, ormanually through conventional patient-operable means. The measures arecollected on a regular, periodic basis for storage in a database alongwith other patient care records. Derived measures are developed from thestored measures. Select stored and derived measures are analyzed andchanges in patient condition are logged. The logged changes are comparedto quantified indicator thresholds to detect findings of respiratorydistress or reduced exercise capacity indicative of the two principalcardiovascular pathophysiological manifestations of congestive heartfailure: elevated left ventricular end diastolic pressure and reducedcardiac output, respectively.

[0010] An embodiment of the present invention is an automated system andmethod for diagnosing and monitoring congestive heart failure andoutcomes thereof. A plurality of monitoring sets is retrieved from adatabase. Each of the monitoring sets includes recorded measuresrelating to patient information recorded on a substantially continuousbasis. A patient status change is determined by comparing at least onerecorded measure from each of the monitoring sets to at least one otherrecorded measure. Both recorded measures relate to the same type ofpatient information. Each patient status change is tested against anindicator threshold corresponding to the same type of patientinformation as the recorded measures that were compared. The indicatorthreshold corresponds to a quantifiable physiological measure of apathophysiology indicative of congestive heart failure.

[0011] A further embodiment is an automated collection and analysispatient care system and method for diagnosing and monitoring congestiveheart failure and outcomes thereof. A plurality of monitoring sets isretrieved from a database. Each monitoring set includes recordedmeasures that each relates to patient information and include eithermedical device measures or derived measures calculable therefrom. Themedical device measures are recorded on a substantially continuousbasis. A set of indicator thresholds is defined. Each indicatorthreshold corresponds to a quantifiable physiological measure of apathophysiology indicative of congestive heart failure and relates tothe same type of patient information as at least one of the recordedmeasures. A congestive heart failure finding is diagnosed. A change inpatient status is determined by comparing at least one recorded measureto at least one other recorded measure with both recorded measuresrelating to the same type of patient information. Each patient statuschange is compared to the indicator threshold corresponding to the sametype of patient information as the recorded measures that were compared.

[0012] A further embodiment is an automated patient care system andmethod for diagnosing and monitoring congestive heart failure andoutcomes thereof. Recorded measures organized into a monitoring set foran individual patient are stored into a database. Each recorded measureis recorded on a substantially continuous basis and relates to at leastone aspect of monitoring reduced exercise capacity and/or respiratorydistress. A plurality of the monitoring sets is periodically retrievedfrom the database. At least one measure related to congestive heartfailure onset, progression, regression, and status quo is evaluated. Apatient status change is determined by comparing at least one recordedmeasure from each of the monitoring sets to at least one other recordedmeasure with both recorded measures relating to the same type of patientinformation. Each patient status change is tested against an indicatorthreshold corresponding to the same type of patient information as therecorded measures that were compared. The indicator thresholdcorresponds to a quantifiable physiological measure of a pathophysiologyindicative of reduced exercise capacity and/or respiratory distress.

[0013] The present invention provides a capability to detect and tracksubtle trends and incremental changes in recorded patient informationfor diagnosing and monitoring congestive heart failure. When coupledwith an enrollment in a remote patient monitoring service having thecapability to remotely and continuously collect and analyze external orimplantable medical device measures, congestive heart failure detection,prevention, and tracking regression from therapeutic maneuvers becomefeasible.

[0014] Still other embodiments of the present invention will becomereadily apparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015]FIG. 1 is a block diagram showing an automated collection andanalysis patient care system for providing diagnosis and monitoring ofcongestive heart failure in accordance with the present invention;

[0016]FIG. 2 is a database schema showing, by way of example, theorganization of a device and derived measures set record for care ofpatients with congestive heart failure stored as part of a patient carerecord in the database of the system of FIG. 1;

[0017]FIG. 3 is a database schema showing, by way of example, theorganization of a quality of life and symptom measures set record forcare of patients with congestive heart failure stored as part of apatient care record in the database of the system of FIG. 1;

[0018]FIG. 4 is a database schema showing, by way of example, theorganization of a combined measures set record for care of patients withcongestive heart failure stored as part of a patient care record in thedatabase of the system of FIG. 1;

[0019]FIG. 5 is a block diagram showing the software modules of theserver system of the system of FIG. 1;

[0020]FIG. 6 is a record view showing, by way of example, a set ofpartial patient care records for care of patients with congestive heartfailure stored in the database of the system of FIG. 1;

[0021]FIG. 7 is a Venn diagram showing, by way of example, peer groupoverlap between the partial patient care records of FIG. 6;

[0022] FIGS. 8A-8B are flow diagrams showing a method for providingdiagnosis and monitoring of congestive heart failure for use inautomated patient care in accordance with the present invention;

[0023]FIG. 9 is a flow diagram showing the routine for retrievingreference baseline sets for use in the method of FIGS. 8A-8B;

[0024]FIG. 10 is a flow diagram showing the routine for retrievingmonitoring sets for use in the method of FIGS. 8A-8B;

[0025] FIGS. 11A-11D are flow diagrams showing the routine for testingthreshold limits for use in the method of FIGS. 8A-8B;

[0026]FIG. 12 is a flow diagram showing the routine for evaluating theonset, progression, regression, and status quo of congestive heartfailure for use in the method of FIGS. 8A-8B;

[0027] FIGS. 13A-13B are flow diagrams showing the routine fordetermining an onset of congestive heart failure for use in the routineof FIG. 12;

[0028] FIGS. 14A-14B are flow diagrams showing the routine fordetermining progression or worsening of congestive heart failure for usein the routine of FIG. 12;

[0029] FIGS. 15A-15B are flow diagrams showing the routine fordetermining regression or improving of congestive heart failure for usein the routine of FIG. 12; and

[0030]FIG. 16 is a flow diagram showing the routine for determiningthreshold stickiness (“hysteresis”) for use in the method of FIG. 12.

Detailed Description

[0031]FIG. 1 is a block diagram showing an automated collection andanalysis patient care system 10 for providing diagnosis and monitoringof congestive heart failure in accordance with the present invention. Anexemplary automated collection and analysis patient care system suitablefor use with the present invention is disclosed in the related,commonly-owned U.S. patent application, Ser. No. 09/324,894, pending,filed Jun. 3, 1999, the disclosure of which is incorporated herein byreference. Preferably, an individual patient 11 is a recipient of animplantable medical device 12, such as, by way of example, an IPG,cardiovascular or heart failure monitor, or therapeutic device, with aset of leads extending into his or her heart and electrodes implantedthroughout the cardiopulmonary system. Alternatively, an externalmonitoring or therapeutic medical device 26, a subcutaneous monitor ordevice inserted into other organs, a cutaneous monitor, or even a manualphysiological measurement device, such as an electrocardiogram or heartrate monitor, could be used. The implantable medical device 12 andexternal medical device 26 include circuitry for recording into ashort-term, volatile memory telemetered signals stored for laterretrieval, which become part of a set of device and derived measures,such as described below, by way of example, with reference to FIG. 2.Exemplary implantable medical devices suitable for use in the presentinvention include the Discovery line of pacemakers, manufactured byGuidant Corporation, Indianapolis, Ind., and the Gem line of ICDs,manufactured by Medtronic Corporation, Minneapolis, Minn.

[0032] The telemetered signals stored in the implantable medical device12 are preferably retrieved upon the completion of an initialobservation period and subsequently thereafter on a continuous, periodic(daily) basis, such as described in the related, commonly-owned U.S.patent application, Ser. No. 09/ 361,332, pending, filed Jul. 26, 1999,the disclosure of which is incorporated herein by reference. Aprogrammer 14, personal computer 18, or similar device for communicatingwith an implantable medical device 12 can be used to retrieve thetelemetered signals. A magnetized reed switch (not shown) within theimplantable medical device 12 closes in response to the placement of awand 13 over the site of the implantable medical device 12. Theprogrammer 14 sends programming or interrogating instructions to andretrieves stored telemetered signals from the implantable medical device12 via RF signals exchanged through the wand 13. Similar communicationmeans are used for accessing the external medical device 26. Oncedownloaded, the telemetered signals are sent via an internetwork 15,such as the Internet, to a server system 16 which periodically receivesand stores the telemetered signals as device measures in patient carerecords 23 in a database 17, as further described below, by way ofexample, with reference to FIGS. 2 and 3. An exemplary programmer 14suitable for use in the present invention is the Model 2901 ProgrammerRecorder Monitor, manufactured by Guidant Corporation, Indianapolis,Ind.

[0033] The patient 11 is remotely monitored by the server system 16 viathe internetwork 15 through the periodic receipt of the retrieved devicemeasures from the implantable medical device 12 or external medicaldevice 26. The patient care records 23 in the database 17 are organizedinto two identified sets of device measures: an optional referencebaseline 26 recorded during an initial observation period and monitoringsets 27 recorded subsequently thereafter. The device measures sets areperiodically analyzed and compared by the server system 16 to indicatorthresholds corresponding to quantifiable physiological measures of apathophysiology indicative of congestive heart failure, as furtherdescribed below with reference to FIG. 5. As necessary, feedback isprovided to the patient 11. By way of example, the feedback includes anelectronic mail message automatically sent by the server system 16 overthe internetwork 15 to a personal computer 18 (PC) situated for localaccess by the patient 11. Alternatively, the feedback can be sentthrough a telephone interface device 19 as an automated voice mailmessage to a telephone 21 or as an automated facsimile message to afacsimile machine 22, both also situated for local access by the patient11. Moreover, simultaneous notifications can also be delivered to thepatient's physician, hospital, or emergency medical services provider 29using similar feedback means to deliver the information.

[0034] The server system 10 can consist of either a single computersystem or a cooperatively networked or clustered set of computersystems. Each computer system is a general purpose, programmed digitalcomputing device consisting of a central processing unit (CPU), randomaccess memory (RAM), non-volatile secondary storage, such as a harddrive or CD ROM drive, network interfaces, and peripheral devices,including user interfacing means, such as a keyboard and display.Program code, including software programs, and data are loaded into theRAM for execution and processing by the CPU and results are generatedfor display, output, transmittal, or storage, as is known in the art.

[0035] The database 17 stores patient care records 23 for eachindividual patient to whom remote patient care is being provided. Eachpatient care record 23 contains normal patient identification andtreatment profile information, as well as medical history, medicationstaken, height and weight, and other pertinent data (not shown). Thepatient care records 23 consist primarily of two sets of data: deviceand derived measures (D&DM) sets 24 a, 24 b and quality of life (QOL)sets 25 a, 25 b, the organization of which are further described belowwith respect to FIGS. 2 and 3, respectively. The device and derivedmeasures sets 24 a, 24 b and quality of life and symptom measures sets25 a, 25 b can be further logically categorized into two potentiallyoverlapping sets. The reference baseline 26 is a special set of deviceand derived reference measures sets 24 a and quality of life and symptommeasures sets 25 a recorded and determined during an initial observationperiod. Monitoring sets 27 are device and derived measures sets 24 b andquality of life and symptom measures sets 25 b recorded and determinedthereafter on a regular, continuous basis. Other forms of databaseorganization are feasible.

[0036] The implantable medical device 12 and, in a more limited fashion,the external medical device 26, record patient information for care ofpatients with congestive heart failure on a regular basis. The recordedpatient information is downloaded and stored in the database 17 as partof a patient care record 23. Further patient information can be derivedfrom recorded data, as is known in the art. FIG. 2 is a database schemashowing, by way of example, the organization of a device and derivedmeasures set record 40 for patient care stored as part of a patient carerecord in the database 17 of the system of FIG. 1. Each record 40 storespatient information which includes a snapshot of telemetered signalsdata which were recorded by the implantable medical device 12 or theexternal medical device 26, for instance, on per heartbeat, binnedaverage or derived bases; measures derived from the recorded devicemeasures; and manually collected information, such as obtained through apatient medical history interview or questionnaire. The followingnon-exclusive information can be recorded for a patient: atrialelectrical activity 41, ventricular electrical activity 42, PR intervalor AV interval 43, QRS measures 44, ST-T wave measures 45, QT interval46, body temperature 47, patient activity score 48, posture 49,cardiovascular pressures 50, pulmonary artery diastolic pressure measure51, cardiac output 52, systemic blood pressure 53, patient geographiclocation (altitude) 54, mixed venous oxygen score 55, arterial oxygenscore 56, pulmonary measures 57, minute ventilation 58, potassium [K+]level 59, sodium [Na+] level 60, glucose level 61, blood urea nitrogen(BUN) and creatinine 62, acidity (pH) level 63, hematocrit 64, hormonallevels 65, cardiac injury chemical tests 66, myocardial blood flow 67,central nervous system (CNS) injury chemical tests 68, central nervoussystem blood flow 69, interventions made by the implantable medicaldevice or external medical device 70, and the relative success of anyinterventions made 71. In addition, the implantable medical device orexternal medical device communicates device-specific information,including battery status, general device status and program settings 72and the time of day 73 for the various recorded measures. Other types ofcollected, recorded, combined, or derived measures are possible, as isknown in the art.

[0037] The device and derived measures sets 24 a, 24 b (shown in FIG.1), along with quality of life and symptom measures sets 25 a, 25 b, asfurther described below with reference to FIG. 3, are continuously andperiodically received by the server system 16 as part of the on-goingpatient care monitoring and analysis function. These regularly collecteddata sets are collectively categorized as the monitoring sets 27 (shownin FIG. 1). In addition, select device and derived measures sets 24 aand quality of life and symptom measures sets 25 a can be designated asa reference baseline 26 at the outset of patient care to improve theaccuracy and meaningfulness of the serial monitoring sets 27. Selectpatient information is collected, recorded, and derived during aninitial period of observation or patient care, such as described in therelated, commonly-owned U.S. patent application, Ser. No. 09/ 361,332,pending, filed Jul. 26, 1999, the disclosure of which is incorporatedherein by reference.

[0038] As an adjunct to remote patient care through the monitoring ofmeasured physiological data via the implantable medical device 12 orexternal medical device 26, quality of life and symptom measures sets 25a can also be stored in the database 17 as part of the referencebaseline 26, if used, and the monitoring sets 27. A quality of lifemeasure is a semi-quantitative self-assessment of an individualpatient's physical and emotional well being and a record of symptoms,such as provided by the Duke Activities Status Indicator. These scoringsystems can be provided for use by the patient 11 on the personalcomputer 18 (shown in FIG. 1) to record his or her quality of lifescores for both initial and periodic download to the server system 16.FIG. 3 is a database schema showing, by way of example, the organizationof a quality of life record 80 for use in the database 17. The followinginformation is recorded for a patient: overall health wellness 81,psychological state 82, activities of daily living 83, work status 84,geographic location 85, family status 86, shortness of breath 87, energylevel 88, exercise tolerance 89, chest discomfort 90, time of day 91,and other quality of life and symptom measures as would be known to oneskilled in the art.

[0039] Other types of quality of life and symptom measures are possible,such as those indicated by responses to the Minnesota Living with HeartFailure Questionnaire described in E. Braunwald, ed., “Heart Disease—ATextbook of Cardiovascular Medicine,” pp. 452-454, W. B. Saunders Co.(1997), the disclosure of which is incorporated herein by reference.Similarly, functional classifications based on the relationship betweensymptoms and the amount of effort required to provoke them can serve asquality of life and symptom measures, such as the New York HeartAssociation (NYHA) classifications I, II, III and IV, also described inIbid.

[0040] The patient may also add non-device quantitative measures, suchas the six-minute walk distance, as complementary data to the device andderived measures sets 24 a, 24 b and the symptoms during the six-minutewalk to quality of life and symptom measures sets 25 a, 25 b.

[0041] On a periodic basis, the patient information stored in thedatabase 17 is analyzed and compared to pre-determined cutoff levels,which, when exceeded, can provide etiological indications of congestiveheart failure symptoms. FIG. 4 is a database schema showing, by way ofexample, the organization of a combined measures set record 95 for usein the database 17. Each record 95 stores patient information obtainedor derived from the device and derived measures sets 24 a, 24 b andquality of life and symptom measures sets 25 a, 25 b as maintained inthe reference baseline 26, if used, and the monitoring sets 27. Thecombined measures set 95 represents those measures most (but notexhaustively or exclusively) relevant to a pathophysiology indicative ofcongestive heart failure and are determined as further described belowwith reference to FIGS. 8A-8B. The following information is stored for apatient: heart rate 96, heart rhythm (e.g., normal sinus vs. atrialfibrillation) 97, pacing modality 98, pulmonary artery diastolicpressure 99, cardiac output 100, arterial oxygen score 101, mixed venousoxygen score 102, respiratory rate 103, transthoracic impedance 104,patient activity score 105, posture 106, exercise tolerance quality oflife and symptom measures 107, respiratory distress quality of life andsymptom measures 108, any interventions made to treat congestive heartfailure 109, including treatment by medical device, via drug infusionadministered by the patient or by a medical device, surgery, and anyother form of medical intervention as is known in the art, the relativesuccess of any such interventions made 110, and time of day 111. Othertypes of comparison measures regarding congestive heart failure arepossible as is known in the art. In the described embodiment, eachcombined measures set 95 is sequentially retrieved from the database 17and processed. Alternatively, each combined measures set 95 could bestored within a dynamic data structure maintained transitorily in therandom access memory of the server system 16 during the analysis andcomparison operations.

[0042]FIG. 5 is a block diagram showing the software modules of theserver system 16 of the system 10 of FIG. 1. Each module is a computerprogram written as source code in a conventional programming language,such as the C or Java programming languages, and is presented forexecution by the CPU of the server system 16 as object or byte code, asis known in the art. The various implementations of the source code andobject and byte codes can be held on a computer-readable storage mediumor embodied on a transmission medium in a carrier wave. The serversystem 16 includes three primary software modules, database module 125,diagnostic module 126, and feedback module 128, which perform integratedfunctions as follows.

[0043] First, the database module 125 organizes the individual patientcare records 23 stored in the database 17 (shown in FIG. 1) andefficiently stores and accesses the reference baseline 26, monitoringsets 27, and patient care data maintained in those records. Any type ofdatabase organization could be utilized, including a flat file system,hierarchical database, relational database, or distributed database,such as provided by database vendors, such as Oracle Corporation,Redwood Shores, Calif.

[0044] Next, the diagnostic module 126 makes findings of congestiveheart failure based on the comparison and analysis of the data measuresfrom the reference baseline 26 and monitoring sets 27. The diagnosticmodule includes three modules: comparison module 130, analysis module131, and quality of life module 132. The comparison module 130 comparesrecorded and derived measures retrieved from the reference baseline 26,if used, and monitoring sets 27 to indicator thresholds 129. Thedatabase 17 stores individual patient care records 23 for patientssuffering from various health disorders and diseases for which they arereceiving remote patient care. For purposes of comparison and analysisby the comparison module 130, these records can be categorized into peergroups containing the records for those patients suffering from similardisorders, as well as being viewed in reference to the overall patientpopulation. The definition of the peer group can be progressivelyrefined as the overall patient population grows. To illustrate, FIG. 6is a record view showing, by way of example, a set of partial patientcare records for care of patients with congestive heart failure storedin the database 17 for three patients, Patient 1, Patient 2, and Patient3. For each patient, three sets of peer measures, X, Y, and Z, areshown. Each of the measures, X, Y, and Z, could be either collected orderived measures from the reference baseline 26, if used, and monitoringsets 27.

[0045] The same measures are organized into time-based sets with Set 0representing sibling measures made at a reference time t=0. Similarly,Set n-2, Set n-1 and Set n each represent sibling measures made at laterreference times t=n-2, t=n-1 and t=n, respectively. Thus, for a givenpatient, such as Patient 1, serial peer measures, such as peer measureX₀ through X_(n), represent the same type of patient informationmonitored over time. The combined peer measures for all patients can becategorized into a health disorder- or disease-matched peer group. Thedefinition of disease-matched peer group is a progressive definition,refined over time as the number of monitored patients grows. Measuresrepresenting different types of patient information, such as measuresX₀, Y₀, and Z₀, are sibling measures. These are measures which are alsomeasured over time, but which might have medically significant meaningwhen compared to each other within a set for an individual patient.

[0046] The comparison module 130 performs two basic forms of comparison.First, individual measures for a given patient can be compared to otherindividual measures for that same patient (self-referencing). Thesecomparisons might be peer-to-peer measures, that is, measures relatingto a one specific type of patient information, projected over time, forinstance, X_(n), X_(n-1), X_(n-2), . . . X₀, or sibling-to-siblingmeasures, that is, measures relating to multiple types of patientinformation measured during the same time period, for a single snapshot,for instance, X_(n),Y_(n), and Z_(n), or projected over time, forinstance, X_(n),Y_(n),Z_(n), X_(n-1), Y_(n-1), Z_(n-1), _(n-2), Y_(n-2),_(n-2), . . . X₀, Y₀,Z₀. Second, individual measures for a given patientcan be compared to other individual measures for a group of otherpatients sharing the same disorder- or disease-specific characteristics(peer group referencing) or to the patient population in general(population referencing). Again, these comparisons might be peer-to-peermeasures projected over time, for instance, X_(n), X_(n′), X_(n″),X_(n-1), X_(n-1′), X_(n-1″), X_(n-2), X_(n-2′), X_(n-2″). . . X₀,X_(0′), X_(0″), or comparing the individual patient's measures to anaverage from the group. Similarly, these comparisons might besibling-to-sibling measures for single snapshots, for instance, X_(n),X_(n ′), X_(n″), Y_(n),Y_(n′),Y_(n″), and Z_(n), Z_(n′), Z_(n″), orprojected over time, for instance, X_(n), X_(′), X_(n″),Y_(n),Y_(n′),Y_(n″), and Z_(n), Z_(n′), Z_(n″), X_(n-1),X_(n-1′),X_(n-1″),Y_(n-1) , Y_(n-1′), Y_(n-1″), Z_(n-1), Z_(n-1′),Z_(n-1″), X-_(n-2), X_(n-2′), X_(n-2″), Y_(n-2), Y_(n-2′), Y_(n-2″),Z_(n-2), Z_(n-2′), Z_(n-2″), X₀, X_(0′), X_(0″),Y₀,Y_(0′), Y_(0″), andZ₀, Z_(0′), Z_(0″). Other forms of comparisons are feasible, includingmultiple disease diagnoses for diseases exhibiting similar abnormalitiesin physiological measures that might result from a second disease butmanifest in different combinations or onset in different temporalsequences.

[0047]FIG. 7 is a Venn diagram showing, by way of example, peer groupoverlap between the partial patient care records 23 of FIG. 1. Eachpatient care record 23 includes characteristics data 350, 351, 352,including personal traits, demographics, medical history, and relatedpersonal data, for patients 1, 2 and 3, respectively. For example, thecharacteristics data 350 for patient 1 might include personal traitswhich include gender and age, such as male and an age between 40-45; ademographic of resident of New York City; and a medical historyconsisting of anterior myocardial infraction, congestive heart failureand diabetes. Similarly, the characteristics data 351 for patient 2might include identical personal traits, thereby resulting in partialoverlap 353 of characteristics data 350 and 351. Similar characteristicsoverlap 354, 355, 356 can exist between each respective patient. Theoverall patient population 357 would include the universe of allcharacteristics data. As the monitoring population grows, the number ofpatients with personal traits matching those of the monitored patientwill grow, increasing the value of peer group referencing. Large peergroups, well matched across all monitored measures, will result in awell known natural history of disease and will allow for more accurateprediction of the clinical course of the patient being monitored. If thepopulation of patients is relatively small, only some traits 356 will beuniformly present in any particular peer group. Eventually, peer groups,for instance, composed of 100 or more patients each, would evolve underconditions in which there would be complete overlap of substantially allsalient data, thereby forming a powerful core reference group for anynew patient being monitored.

[0048] Referring back to FIG. 5, the analysis module 131 analyzes theresults from the comparison module 130, which are stored as a combinedmeasures set 95 (not shown), to a set of indicator thresholds 129, asfurther described below with reference to FIGS. 8A-8B. Similarly, thequality of life module 132 compares quality of life and symptom measuresset 25 a, 25 b from the reference baseline 26 and monitoring sets 27,the results of which are incorporated into the comparisons performed bythe analysis module 131, in part, to either refute or support thefindings based on physiological “hard” data. Finally, the feedbackmodule 128 provides automated feedback to the individual patient based,in part, on the patient status indicator 127 generated by the diagnosticmodule 126. As described above, the feedback could be by electronic mailor by automated voice mail or facsimile. The feedback can also includenormalized voice feedback, such as described in the related,commonly-owned U.S. patent application, Ser. No. 09/361,777, pending,filed Jul. 26, 1999, the disclosure of which is incorporated herein byreference. In addition, the feedback module 128 determines whether anychanges to interventive measures are appropriate based on thresholdstickiness (“hysteresis”) 133, as further described below with referenceto FIG. 16. The threshold stickiness 133 can prevent fickleness indiagnostic routines resulting from transient, non-trending andnon-significant fluctuations in the various collected and derivedmeasures in favor of more certainty in diagnosis. In a furtherembodiment of the present invention, the feedback module 128 includes apatient query engine 134 which enables the individual patient 11 tointeractively query the server system 16 regarding the diagnosis,therapeutic maneuvers, and treatment regimen. Conversely, the patientquery engines 134, found in interactive expert systems for diagnosingmedical conditions, can interactively query the patient. Using thepersonal computer 18 (shown in FIG. 1), the patient can have aninteractive dialogue with the automated server system 16, as well ashuman experts as necessary, to self assess his or her medical condition.Such expert systems are well known in the art, an example of which isthe MYCIN expert system developed at Stanford University and describedin Buchanan, B. & Shortlife, E., “RULE-BASED EXPERT SYSTEMS. The MYCINExperiments of the Stanford Heuristic Programming Project,”Addison-Wesley (1984). The various forms of feedback described abovehelp to increase the accuracy and specificity of the reporting of thequality of life and symptomatic measures.

[0049] FIGS. 8A-8B are flow diagrams showing a method for providingdiagnosis and monitoring of congestive heart failure for use inautomated patient care 135 in accordance with the present invention.First, the indicator thresholds 129 (shown in FIG. 5) are set (block136) by defining a quantifiable physiological measure of apathophysiology indicative of congestive heart failure and relating tothe each type of patient information in the combined device and derivedmeasures set 95 (shown in FIG. 4). The actual values of each indicatorthreshold can be finite cutoff values, weighted values, or statisticalranges, as discussed below with reference to FIGS. 11A-11D. Next, thereference baseline 26 (block 137) and monitoring sets 27 (block 138) areretrieved from the database 17, as further described below withreference to FIGS. 9 and 10, respectively. Each measure in the combineddevice and derived measures set 95 is tested against the thresholdlimits defined for each indicator threshold 129 (block 139), as furtherdescribed below with reference to FIGS. 11A-11D. The potential onset,progression, regression, or status quo of congestive heart failure isthen evaluated (block 140) based upon the findings of the thresholdlimits tests (block 139), as further described below with reference toFIGS. 13A-13B, 14A-14B, 15A-15B.

[0050] In a further embodiment, multiple near-simultaneous disorders areconsidered in addition to primary congestive heart failure. Primarycongestive heart failure is defined as the onset or progression ofcongestive heart failure without obvious inciting cause. Secondarycongestive heart failure is defined as the onset or progression ofcongestive heart failure (in a patient with or without pre-existingcongestive heart failure) from another disease process, such as coronaryinsufficiency, respiratory insufficiency, atrial fibrillation, and soforth. Other health disorders and diseases can potentially share thesame forms of symptomatology as congestive heart failure, such asmyocardial ischemia, respiratory insufficiency, pneumonia, exacerbationof chronic bronchitis, renal failure, sleep-apnea, stroke, anemia,atrial fibrillation, other cardiac arrhythmias, and so forth. If morethan one abnormality is present, the relative sequence and magnitude ofonset of abnormalities in the monitored measures becomes most importantin sorting and prioritizing disease diagnosis and treatment.

[0051] Thus, if other disorders or diseases are being cross-referencedand diagnosed (block 141), their status is determined (block 142). Inthe described embodiment, the operations of ordering and prioritizingmultiple near-simultaneous disorders (box 151) by the testing ofthreshold limits and analysis in a manner similar to congestive heartfailure as described above, preferably in parallel to the presentdetermination, is described in the related, commonly-owned U.S. patentapplication, Ser. No. ______, entitled “Automated Collection AndAnalysis Patient Care System And Method For Ordering And PrioritizingMultiple Health Disorders To Identify An Index Disorder,” pending, filedNov. 16, 1999, the disclosure of which is incorporated herein byreference. If congestive heart failure is due to an obvious incitingcause, i.e., secondary congestive heart failure, (block 143), anappropriate treatment regimen for congestive heart failure asexacerbated by other disorders is adopted that includes treatment ofsecondary disorders, e.g., myocardial ischemia, respiratoryinsufficiency, atrial fibrillation, and so forth (block 144) and asuitable patient status indicator 127 for congestive heart failure isprovided (block 146) to the patient. Suitable devices and approaches todiagnosing and treating myocardial infarction, respiratory distress andatrial fibrillation are described in related, commonly-owned U.S. patentapplications, Ser. No. ______, entitled “Automated Collection AndAnalysis Patient Care System And Method For Diagnosing And MonitoringMyocardial Ischemia And Outcomes Thereof,” pending, filed Nov. 16, 1999;Ser. No. ______, entitled “Automated Collection And Analysis PatientCare System And Method For Diagnosing And Monitoring RespiratoryInsufficiency And Outcomes Thereof,” pending, filed Nov. 16, 1999; andSer. No. ______, entitled “Automated Collection And Analysis PatientCare System And Method For Diagnosing And Monitoring The Outcomes OfAtrial Fibrillation” pending, filed Nov. 16, 1999, the disclosures ofwhich are incorporated herein by reference.

[0052] Otherwise, if primary congestive heart failure is indicated(block 143), a primary treatment regimen is followed (block 145). Apatient status indicator 127 for congestive heart failure is provided(block 146) to the patient regarding physical well-being, diseaseprognosis, including any determinations of disease onset, progression,regression, or status quo, and other pertinent medical and generalinformation of potential interest to the patient.

[0053] Finally, in a further embodiment, if the patient submits a queryto the server system 16 (block 147), the patient query is interactivelyprocessed by the patient query engine (block 148). Similarly, if theserver elects to query the patient (block 149), the server query isinteractively processed by the server query engine (block 150). Themethod then terminates if no further patient or server queries aresubmitted.

[0054]FIG. 9 is a flow diagram showing the routine for retrievingreference baseline sets 137 for use in the method of FIGS. 8A-8B. Thepurpose of this routine is to retrieve the appropriate referencebaseline sets 26, if used, from the database 17 based on the types ofcomparisons being performed. First, if the comparisons are selfreferencing with respect to the measures stored in the individualpatient care record 23 (block 152), the reference device and derivedmeasures set 24 a and reference quality of life and symptom measures set25 a, if used, are retrieved for the individual patient from thedatabase 17 (block 153). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 154),the reference device and derived measures set 24 a and reference qualityof life and symptom measures set 25 a, if used, are retrieved from eachpatient care record 23 for the peer group from the database 17 (block155). Data for each measure (e.g., minimum, maximum, averaged, standarddeviation (SD), and trending data) from the reference baseline 26 forthe peer group is then calculated (block 156). Finally, if thecomparisons are population referencing with respect to measures storedin the patient care records 23 for the overall patient population (block157), the reference device and derived measures set 24 a and referencequality of life and symptom measures set 25 a, if used, are retrievedfrom each patient care record 23 from the database 17 (block 158).Minimum, maximum, averaged, standard deviation, and trending data andother numerical processes using the data, as is known in the art, foreach measure from the reference baseline 26 for the peer group is thencalculated (block 159). The routine then returns.

[0055]FIG. 10 is a flow diagram showing the routine for retrievingmonitoring sets 138 for use in the method of FIGS. 8A-8B. The purpose ofthis routine is to retrieve the appropriate monitoring sets 27 from thedatabase 17 based on the types of comparisons being performed. First, ifthe comparisons are self referencing with respect to the measures storedin the individual patient care record 23 (block 160), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved for the individual patient from thedatabase 17 (block 161). Next, if the comparisons are peer groupreferencing with respect to measures stored in the patient care records23 for a health disorder- or disease-specific peer group (block 162),the device and derived measures set 24 b and quality of life and symptommeasures set 25 b, if used, are retrieved from each patient care record23 for the peer group from the database 17 (block 163). Data for eachmeasure (e.g., minimum, maximum, averaged, standard deviation, andtrending data) from the monitoring sets 27 for the peer group is thencalculated (block 164). Finally, if the comparisons are populationreferencing with respect to measures stored in the patient care records23 for the overall patient population (block 165), the device andderived measures set 24 b and quality of life and symptom measures set25 b, if used, are retrieved from each patient care record 23 from thedatabase 17 (block 166). Minimum, maximum, averaged, standard deviation,and trending data and other numerical processes using the data, as isknown in the art, for each measure from the monitoring sets 27 for thepeer group is then calculated (block 167). The routine then returns.

[0056] FIGS. 11A-11D are flow diagrams showing the routine for testingthreshold limits 139 for use in the method of FIG. 8A and 8B. Thepurpose of this routine is to analyze, compare, and log any differencesbetween the observed, objective measures stored in the referencebaseline 26, if used, and the monitoring sets 27 to the indicatorthresholds 129. Briefly, the routine consists of tests pertaining toeach of the indicators relevant to diagnosing and monitoring congestiveheart failure. The threshold tests focus primarily on: (1) changes toand rates of change for the indicators themselves, as stored in thecombined device and derived measures set 95 (shown in FIG. 4) or similardata structure; and (2) violations of absolute threshold limits whichtrigger an alert. The timing and degree of change may vary with eachmeasure and with the natural fluctuations noted in that measure duringthe reference baseline period. In addition, the timing and degree ofchange might also vary with the individual and the natural history of ameasure for that patient.

[0057] One suitable approach to performing the threshold tests uses astandard statistical linear regression technique using a least squareserror fit. The least squares error fit can be calculated as follows:

y=β ₀+β₁ x   (1)

[0058] $\begin{matrix}{\beta = \frac{{SS}_{xy}}{{SS}_{xx}}} & (2)\end{matrix}$

$\begin{matrix}{{SS}_{xy} = {{\sum\limits_{i = 1}^{n}{x_{i}y_{i}}} - \frac{\left( {\sum\limits_{i = 1}^{n}x_{i}} \right)\left( {\sum\limits_{i = 1}^{n}y_{i}} \right)}{n}}} & (3) \\{{SS}_{xx} = {{\sum\limits_{i = 1}^{n}x_{i}^{2}} - \frac{\left( {\sum\limits_{i = 1}^{n}x_{i}} \right)^{2}}{n}}} & (4)\end{matrix}$

[0059] where n is the total number of measures, x_(i) is the time of dayfor measure i, and y_(i) is the value of measure i, β₁ is the slope, andβ₀ is the y-intercept of the least squares error line. A positive slopeβ₁ indicates an increasing trend, a negative slope β₁ indicates adecreasing trend, and no slope indicates no change in patient conditionfor that particular measure. A predicted measure value can be calculatedand compared to the appropriate indicator threshold 129 for determiningwhether the particular measure has either exceeded an acceptablethreshold rate of change or the absolute threshold limit.

[0060] For any given patient, three basic types of comparisons betweenindividual measures stored in the monitoring sets 27 are possible: selfreferencing, peer group, and general population, as explained above withreference to FIG. 6. In addition, each of these comparisons can includecomparisons to individual measures stored in the pertinent referencebaselines 24.

[0061] The indicator thresholds 129 for detecting a trend indicatingprogression into a state of congestive heart failure or a state ofimminent or likely congestive heart failure, for example, over a oneweek time period, can be as follows:

[0062] (1) Respiratory rate (block 170): If the respiratory rate hasincreased over 1.0 SD from the mean respiratory rate in the referencebaseline 26 (block 171), the increased respiratory rate and time spanover which it occurs are logged in the combined measures set 95 (block172).

[0063] (2) Heart rate (block 173): If the heart rate has increased over1.0 SD from the mean heart rate in the reference baseline 26 (block174), the increased heart rate and time span over which it occurs arelogged in the combined measures set 95 (block 175).

[0064] (3) Pulmonary artery diastolic pressure (PADP) (block 176)reflects left ventricular filling pressure and is a measure of leftventricular dysfunction. Ideally, the left ventricular end diastolicpressure (LVEDP) should be monitored, but in practice is difficult tomeasure. Consequently, without the LVEDP, the PADP, or derivativesthereof, is suitable for use as an alternative to LVEDP in the presentinvention. If the PADP has increased over 1.0 SD from the mean PADP inthe reference baseline 26 (block 177), the increased PADP and time spanover which that increase occurs, are logged in the combined measures set95 (block 178). Other cardiac pressures or derivatives could also apply.

[0065] (4) Transthoracic impedance (block 179): If the transthoracicimpedance has decreased over 1.0 SD from the mean transthoracicimpedance in the reference baseline 26 (block 180), the decreasedtransthoracic impedance and time span are logged in the combinedmeasures set 95 (block 181).

[0066] (5) Arterial oxygen score (block 182): If the arterial oxygenscore has decreased over 1.0 SD from the arterial oxygen score in thereference baseline 26 (block 183), the decreased arterial oxygen scoreand time span are logged in the combined measures set 95 (block 184).

[0067] (6) Venous oxygen score (block 185): If the venous oxygen scorehas decreased over 1.0 SD from the mean venous oxygen score in thereference baseline 26 (block 186), the decreased venous oxygen score andtime span are logged in the combined measures set 95 (block 187).

[0068] (7) Cardiac output (block 188): If the cardiac output hasdecreased over 1.0 SD from the mean cardiac output in the referencebaseline 26 (block 189), the decreased cardiac output and time span arelogged in the combined measures set 95 (block 190).

[0069] (8) Patient activity score (block 191): If the mean patientactivity score has decreased over 1.0 SD from the mean patient activityscore in the reference baseline 26 (block 192), the decreased patientactivity score and time span are logged in the combined measures set 95(block 193).

[0070] (9) Exercise tolerance quality of life (QOL) measures (block194): If the exercise tolerance QOL has decreased over 1.0 SD from themean exercise tolerance in the reference baseline 26 (block 195), thedecrease in exercise tolerance and the time span over which it occursare logged in the combined measures set 95 (block 196).

[0071] (10) Respiratory distress quality of life (QOL) measures (block197): If the respiratory distress QOL measure has deteriorated by morethan 1.0 SD from the mean respiratory distress QOL measure in thereference baseline 26 (block 198), the increase in respiratory distressand the time span over which it occurs are logged in the combinedmeasures set 95 (block 199).

[0072] (11) Atrial fibrillation (block 200): The presence or absence ofatrial fibrillation (AF) is determined and, if present (block 201),atrial fibrillation is logged (block 202).

[0073] (12) Rhythm changes (block 203): The type and sequence of rhythmchanges is significant and is determined based on the timing of therelevant rhythm measure, such as sinus rhythm. For instance, a findingthat a rhythm change to atrial fibrillation precipitated circulatorymeasures changes can indicate therapy directions against atrialfibrillation rather than primary progression of congestive heartfailure. Thus, if there are rhythm changes (block 204), the sequence ofthe rhythm changes and time span are logged (block 205).

[0074] Note also that an inversion of the indicator thresholds 129defined above could similarly be used for detecting a trend in diseaseregression. One skilled in the art would recognize that these measureswould vary based on whether or not they were recorded during rest orduring activity and that the measured activity score can be used toindicate the degree of patient rest or activity. The patient activityscore can be determined via an implantable motion detector, for example,as described in U.S. Pat. No. 4,428,378, issued Jan. 31, 1984, toAnderson et al., the disclosure of which is incorporated herein byreference.

[0075] The indicator thresholds 129 for detecting a trend towards astate of congestive heart failure can also be used to declare, a priori,congestive heart failure present, regardless of pre-existing trend datawhen certain limits are established, such as:

[0076] (1) An absolute limit of PADP (block 170) exceeding 25 mm Hg isan a priori definition of congestive heart failure from left ventricularvolume overload.

[0077] (2) An absolute limit of indexed cardiac output (block 191)falling below 2.0 l/min/m² is an a priori definition of congestive heartfailure from left ventricular myocardial pump failure when recorded inthe absence of intravascular volume depletion (e.g., from hemorrhage,septic shock, dehydration, etc.) as indicated by a reduced PADP (e.g.,<10 mmHg).

[0078]FIG. 12 is a flow diagram showing the routine for evaluating theonset, progression, regression and status quo of congestive heartfailure 140 for use in the method of FIG. 8A and 8B. The purpose of thisroutine is to evaluate the presence of sufficient indicia to warrant adiagnosis of the onset, progression, regression, and status quo ofcongestive heart failure. Quality of life and symptom measures set 25 a,25 b can be included in the evaluation (block 230) by determiningwhether any of the individual quality of life and symptom measures set25 a, 25 b have changed relative to the previously collected quality oflife and symptom measures from the monitoring sets 27 and the referencebaseline 26, if used. For example, an increase in the shortness ofbreath measure 87 and exercise tolerance measure 89 would corroborate afinding of congestive heart failure. Similarly, a transition from NYHAClass II to NYHA Class III would indicate deterioration or, conversely,a transition from NYHA Class III to NYHA Class II status would indicateimprovement or progress. Incorporating the quality of life and symptommeasures set 25 a, 25 b into the evaluation can help, in part, to refuteor support findings based on physiological data. Next, a determinationas to whether any changes to interventive measures are appropriate basedon threshold stickiness (“hysteresis”) is made (block 231), as furtherdescribed below with reference to FIG. 16.

[0079] The routine returns upon either the determination of a finding orelimination of all factors as follows. If a finding of congestive heartfailure was not previously diagnosed (block 232), a determination ofdisease onset is made (block 233), as further described below withreference to FIGS. 13A-13C. Otherwise, if congestive heart failure waspreviously diagnosed (block 232), a further determination of eitherdisease progression or worsening (block 234) or regression or improving(block 235) is made, as further described below with reference to FIGS.14A-14C and 15A-15C, respectively. If, upon evaluation, neither diseaseonset (block 233), worsening (block 234) or improving (block 235) isindicated, a finding of status quo is appropriate (block 236) and noted(block 235). Otherwise, congestive heart failure and the relatedoutcomes are actively managed (block 238) through the administration of,non-exclusively, preload reduction, afterload reduction, diuresis,beta-blockade, inotropic agents, electrolyte management, electricaltherapies, mechanical therapies, and other therapies as are known in theart. The management of congestive heart failure is described, by way ofexample, in E. Braunwald, ed., “Heart Disease—A Textbook ofCardiovascular Medicine,” Ch. 17, W. B. Saunders Co. (1997), thedisclosure of which is incorporated herein by reference. The routinethen returns.

[0080] FIGS. 13A-13B are flow diagrams showing the routine fordetermining an onset of congestive heart failure 232 for use in theroutine of FIG. 12. Congestive heart failure is possible based on twogeneral symptom categories: reduced exercise capacity (block 244) andrespiratory distress (block 250). An effort is made to diagnosecongestive heart failure manifesting primarily as resulting in reducedexercise capacity (block 244) and/or increased respiratory distress(block 250). Several factors need be indicated to warrant a diagnosis ofcongestive heart failure onset, as well as progression, as summarizedbelow with reference to FIGS. 13A-13B in TABLE 1, Disease Onset orProgression. Reduced exercise capacity generally serves as a marker oflow cardiac output and respiratory distress as a marker of increasedleft ventricular end diastolic pressure. The clinical aspects ofcongestive heart failure are described, by way of example, in E.Braunwald, ed., “Heart Disease—A Textbook of Cardiovascular Medicine,”Chs. 1 and 15, W. B. Saunders Co. (1997), the disclosure of which isincorporated herein by reference.

[0081] Per TABLE 1, multiple individual indications (blocks 240-243,245-250) should be present for the two principal findings of congestiveheart failure related reduced exercise capacity (block 244), orcongestive heart failure related respiratory distress (block 250), to beindicated, both for disease onset or progression. A bold “++” symbolindicates a primary key finding which is highly indicative of congestiveheart failure, that is, reduced exercise capacity or respiratorydistress, a bold “+” symbol indicates a secondary key finding which isstrongly suggestive, and a “±” symbol indicates a tertiary permissivefinding which may be present or absent. The presence of primary keyfindings alone can be sufficient to indicate an onset of congestiveheart failure and secondary key findings serve to corroborate diseaseonset. Note the presence of any abnormality can trigger an analysis forthe presence or absence of secondary disease processes, such as thepresence of atrial fibrillation or pneumonia. Secondary diseaseconsiderations can be evaluated using the same indications (see, e.g.,blocks 141-144 of FIGS. 8A-8B), but with adjusted indicator thresholds129 (shown in FIG. 5) triggered at a change of 0.5 SD, for example,instead of 1.0 SD.

[0082] In the described embodiment, the reduced exercise capacity andrespiratory distress findings (blocks 244, 250) can be established byconsolidating the individual indications (blocks 240-243, 245-250) inseveral ways. First, in a preferred embodiment, each individualindication (blocks 240-243, 245-250) is assigned a scaled index valuecorrelating with the relative severity of the indication. For example,decreased cardiac output (block 240) could be measured on a scale from‘1’ to ‘5’ wherein a score of ‘1’ indicates no change in cardiac outputfrom the reference point, a score of ‘2’ indicates a change exceeding0.5 SD, a score of ‘3’ indicates a change exceeding 1.0 SD, a score of‘4’ indicates a change exceeding 2.0 SD, and a score of ‘5’ indicates achange exceeding 3.0 SD. The index value for each of the individualindications (blocks 240-243, 245-250) can then either be aggregated oraveraged with a result exceeding the aggregate or average maximumindicating an appropriate congestive heart failure finding.

[0083] Preferably, all scores are weighted depending upon theassignments made from the measures in the reference baseline 26. Forinstance, transthoracic impedance 104 (shown in FIG. 4) could beweighted more importantly than respiratory rate 103 if the respiratoryrate in the reference baseline 26 is particularly high at the outset,making the detection of further disease progression from increases inrespiratory rate, less sensitive. In the described embodiment, cardiacoutput 100 receives the most weight in determining a reduced exercisecapacity finding whereas pulmonary artery diastolic pressure 99 receivesthe most weight in determining a respiratory distress or dyspneafinding.

[0084] Alternatively, a simple binary decision tree can be utilizedwherein each of the individual indications (blocks 240-243, 245-250) iseither present or is not present. All or a majority of the individualindications (blocks 240-243, 245-250) should be present for the relevantcongestive heart failure finding to be affirmed.

[0085] Other forms of consolidating the individual indications (blocks240-243, 245-250) are feasible.

[0086] FIGS. 14A-14B are flow diagrams showing the routine fordetermining a progression or worsening of congestive heart failure 233for use in the routine of FIG. 12. The primary difference between thedeterminations of disease onset, as described with reference to FIGS.13A-13B, and disease progression is the evaluation of changes indicatedin the same factors present in a disease onset finding. Thus, a revisedcongestive heart failure finding is possible based on the same twogeneral symptom categories: reduced exercise capacity (block 264) andrespiratory distress (block 271). The same factors which need beindicated to warrant a diagnosis of congestive heart failure onset areevaluated to determine disease progression, as summarized below withreference to FIGS. 14A-14B in TABLE 1, Disease Onset or Progression.

[0087] Similarly, these same factors trending in opposite directionsfrom disease onset or progression, are evaluated to determine diseaseregression or improving, as summarized below with reference to FIGS.15A-15B in TABLE 2, Disease Regression. Per TABLE 2, multiple individualindications (blocks 260-263, 265-270) should be present for the twoprincipal findings of congestive heart failure related reduced exercisecapacity (block 264), or congestive heart failure related respiratorydistress (block 271), to indicate disease regression. As in TABLE 1, abold “++” symbol indicates a primary key finding which is highlyindicative of congestive heart failure, that is, reduced exercisecapacity or respiratory distress, a bold “+” symbol indicates asecondary key finding which is strongly suggestive, and a “±” symbolindicates a tertiary permissive finding which may be present or absent.The more favorable the measure, the more likely regression of congestiveheart failure is indicated.

[0088]FIG. 16 is a flow diagram showing the routine for determiningthreshold stickiness (“hysteresis”) 231 for use in the method of FIG.12. Stickiness, also known as hysteresis, is a medical practice doctrinewhereby a diagnosis or therapy will not be changed based upon small ortemporary changes in a patient reading, even though those changes mighttemporarily move into a new zone of concern. For example, if a patientmeasure can vary along a scale of ‘1’ to ‘10’ with ‘10’ being worse, atransient reading of ‘6,’ standing alone, on a patient who hasconsistently indicated a reading of ‘5’ for weeks will not warrant achange in diagnosis without a definitive prolonged deterioration firstbeing indicated. Stickiness dictates that small or temporary changesrequire more diagnostic certainty, as confirmed by the persistence ofthe changes, than large changes would require for any of the monitored(device) measures. Stickiness also makes reversal of importantdiagnostic decisions, particularly those regarding life-threateningdisorders, more difficult than reversal of diagnoses of modest import.As an example, automatic external defibrillators (AEDs) manufactured byHeartstream, a subsidiary of Agilent Technologies, Seattle, Wash.,monitor heart rhythms and provide interventive shock treatment for thediagnosis of ventricular fibrillation. Once diagnosis of ventricularfibrillation and a decision to shock the patient has been made, apattern of no ventricular fibrillation must be indicated for arelatively prolonged period before the AED changes to a “no-shock”decision. As implemented in this AED example, stickiness mandatescertainty before a decision to shock is disregarded.

[0089] In practice, stickiness also dictates that acute deteriorationsin disease state are treated aggressively while chronic, more slowlyprogressing disease states are treated in a more tempered fashion. Thus,if the patient status indicates a status quo (block 330), no changes intreatment or diagnosis are indicated and the routine returns. Otherwise,if the patient status indicates a change away from status quo (block330), the relative quantum of change and the length of time over whichthe change has occurred is determinative. If the change of approximately0.5 SD has occurred over the course of about one month (block 331), agradually deteriorating condition exists (block 332) and a very tempereddiagnostic, and if appropriate, treatment program is undertaken. If thechange of approximately 1.0 SD has occurred over the course of about oneweek (block 333), a more rapidly deteriorating condition exists (block334) and a slightly more aggressive diagnostic, and if appropriate,treatment program is undertaken. If the change of approximately 2.0 SDhas occurred over the course of about one day (block 335), an urgentlydeteriorating condition exists (block 336) and a moderately aggressivediagnostic, and if appropriate, treatment program is undertaken. If thechange of approximately 3.0 SD has occurred over the course of about onehour (block 337), an emergency condition exists (block 338) and animmediate diagnostic, and if appropriate, treatment program isundertaken as is practical. Finally, if the change and duration falloutside the aforementioned ranges (blocks 331-338), an exceptionalcondition exists (block 339) and the changes are reviewed manually, ifnecessary. The routine then returns. These threshold limits and timeranges may then be adapted depending upon patient history and peer-groupguidelines.

[0090] The form of the revised treatment program depends on the extentto which the time span between changes in the device measures exceed thethreshold stickiness 133 (shown in FIG. 5) relating to that particulartype of device measure. For example, threshold stickiness 133 indicatorfor monitoring a change in heart rate in a chronic patient sufferingfrom congestive heart failure might be 10% over a week. Consequently, achange in average heart rate 96 (shown in FIG. 4) from 80 bpm to 95 bpmover a seven day period, where a 14 beat per minute average change wouldequate to a 1.0 SD change, would exceed the threshold stickiness 133 andwould warrant a revised medical diagnosis perhaps of diseaseprogression. One skilled in the art would recognize the indications ofacute versus chronic disorders which will vary upon the type of disease,patient health status, disease indicators, length of illness, and timingof previously undertaken interventive measures, plus other factors.

[0091] The present invention provides several benefits. One benefit isimproved predictive accuracy from the outset of patient care when areference baseline is incorporated into the automated diagnosis. Anotherbenefit is an expanded knowledge base created by expanding themethodologies applied to a single patient to include patient peer groupsand the overall patient population. Collaterally, the informationmaintained in the database could also be utilized for the development offurther predictive techniques and for medical research purposes. Yet afurther benefit is the ability to hone and improve the predictivetechniques employed through a continual reassessment of patient therapyoutcomes and morbidity rates.

[0092] Other benefits include an automated, expert system approach tothe cross-referral, consideration, and potential finding or eliminationof other diseases and health disorders with similar or relatedetiological indicators and for those other disorders that may have animpact on congestive heart failure. Although disease specific markerswill prove very useful in discriminating the underlying cause ofsymptoms, many diseases, other than congestive heart failure, will altersome of the same physiological measures indicative of congestive heartfailure. Consequently, an important aspect of considering the potentialimpact of other disorders will be, not only the monitoring of diseasespecific markers, but the sequencing of change and the temporalevolution of more general physiological measures, for examplerespiratory rate, arterial oxygenation, and cardiac output, to reflectdisease onset, progression or regression in more than one type ofdisease process.

[0093] While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention. TABLE 1 Disease Onset or Progression. Congestive HeartCongestive Heart Failure (Reduced Failure (Increasing Exercise Capacity)Respiratory Distress) Finding (block Finding (block IndividualIndications 244, 274) 250, 280) Decreased cardiac output ++ ± (blocks240, 260) Decreased mixed venous + ± oxygen score (blocks 241, 261)Decreased patient activity + ± score (block 243, 263) Increasedpulmonary artery ± ++ diastolic pressure (PADP) (block 245, 265)Increased respiratory rate ± + (block 246, 266) Decreased transthoracic± + impedance (TTZ) (block 248, 268)

[0094] TABLE 2 Disease Regression. Congestive Heart Congestive HeartFailure Failure (Decreasing (Improving Exercise Respiratory Capacity)Finding Distress) Finding Individual Indications (block 304) (block 310)Increased cardiac output ++ ± (block 300) Increased mixed venous + ±oxygen score (block 301) Increased patient activity + ± score (block303) Decreased pulmonary ± ++ artery diastolic pressure (PADP) (block305) Decreased respiratory rate ± + (block 306) Increased transthoracic± + impedance (TTZ) (block 308)

What is claimed is:
 1. A system for providing diagnosis and monitoringof congestive heart failure for use in automated patient care,comprising: a comparison module comparing at least one recordedphysiological measure to at least one other recorded physiologicalmeasure on a substantially regular basis to quantify a change in patientpathophysiological status for equivalent patient information; and ananalysis module evaluating an absence, an onset, a progression, aregression, and a status quo of congestive heart failure dependent uponthe change in patient pathophysiological status.
 2. A system accordingto claim 1, further comprising: a diagnostic module comparing the changein patient pathophysiological status to an indicator thresholdcorresponding to a quantifiable physiological measure indicative ofcongestive heart failure.
 3. A system according to claim 1, furthercomprising: a database module retrieving the at least one recordedphysiological measure and the at least one other recorded physiologicalmeasure from monitoring sets stored in a database.
 4. A system accordingto claim 3, further comprising: a server system collecting the at leastone recorded physiological measure and the at least one other recordedphysiological measure into each monitoring set recorded on asubstantially continuous basis or derived therefrom.
 5. A systemaccording to claim 4, further comprising: at least one of an implantablemedical device and an external medical device recording the at least onerecorded physiological measure and the at least one other recordedphysiological measure.
 6. A system according to claim 1, furthercomprising: the analysis module evaluating an absence, an onset, aprogression, a regression, and a status quo of diseases other thancongestive heart failure dependent upon the change in patientpathophysiological status.
 7. A system according to claim 1, furthercomprising: a diagnostic module comparing at least one recorded qualityof life measure to at least one other recorded quality of life measureon a substantially regular basis to qualify a change in patientpathophysiological status.
 8. A system according to claim 1, furthercomprising: a stored stickiness indicator defined for at least onephysiological measure corresponding to a temporal boundary on one ofpatient diagnosis and treatment; a diagnostic module timing each changein patient pathophysiological status for the equivalent patientinformation and determining one of a revised patient diagnosis andtreatment responsive to each change in patient pathophysiological statuswith a timing exceeding the stickiness indicator.
 9. A system accordingto claim 1, further comprising: a diagnostic module comparing the changein patient pathophysiological status to a reference baseline comprisingrecorded physiological measures recorded during an initial time period.10. A system according to claim 1, further comprising: a diagnosticmodule comparing the change in patient pathophysiological status toequivalent patient information from at least one of an individualpatient, a peer group, and a overall patient population.
 11. A methodfor providing diagnosis and monitoring of congestive heart failure foruse in automated patient care, comprising: comparing at least onerecorded physiological measure to at least one other recordedphysiological measure on a substantially regular basis to quantify achange in patient pathophysiological status for equivalent patientinformation; and evaluating an absence, an onset, a progression, aregression, and a status quo of congestive heart failure dependent uponthe change in patient pathophysiological status.
 12. A method accordingto claim 11, further comprising: comparing the change in patientpathophysiological status to an indicator threshold corresponding to aquantifiable physiological measure indicative of congestive heartfailure.
 13. A method according to claim 11, further comprising:retrieving the at least one recorded physiological measure and the atleast one other recorded physiological measure from monitoring setsstored in a database.
 14. A method according to claim 13, furthercomprising: collecting the at least one recorded physiological measureand the at least one other recorded physiological measure into eachmonitoring set recorded on a substantially continuous basis or derivedtherefrom.
 15. A method according to claim 14, further comprising:recording the at least one recorded physiological measure and the atleast one other recorded physiological measure with at least one of animplantable medical device and an external medical device.
 16. A methodaccording to claim 11, further comprising: evaluating an absence, anonset, a progression, a regression, and a status quo of diseases otherthan congestive heart failure dependent upon the change in patientpathophysiological status.
 17. A method according to claim 11, furthercomprising: comparing at least one recorded quality of life measure toat least one other recorded quality of life measure on a substantiallyregular basis to qualify a change in patient pathophysiological status.18. A method according to claim 11, further comprising: defining astickiness indicator for at least one physiological measurecorresponding to a temporal boundary on one of patient diagnosis andtreatment; timing each change in patient pathophysiological status forthe equivalent patient information; and determining one of a revisedpatient diagnosis and treatment responsive to each change in patientpathophysiological status with a timing exceeding the stickinessindicator.
 19. A method according to claim 11, further comprising:comparing the change in patient pathophysiological status to a referencebaseline comprising recorded physiological measures recorded during aninitial time period.
 20. A method according to claim 11, furthercomprising: comparing the change in patient pathophysiological status toequivalent patient information from at least one of an individualpatient, a peer group, and a overall patient population.
 21. Acomputer-readable storage medium for a device holding code forperforming the method according to claims 11, 12, 13, 14, 15, 16, 17,18, 19, or
 20. 22. A system for analyzing a patient status forcongestive heart failure for use in automated patient care, comprising:a server system receiving a set of one or more physiological measuresrelating to patient information recorded on a substantially continuousbasis or derived therefrom; a database module storing the physiologicalmeasures set into a patient care record for an individual patient into adatabase; and an analyzer analyzing one or more of the physiologicalmeasures in the physiological measures set relative to one or more otherphysiological measures to determine a pathophysiology indicating anabsence, an onset, a progression, a regression, and a status quo ofcongestive heart failure.
 23. A system according to claim 22, furthercomprising: the analyzer analyzing the physiological measures in thephysiological measures set relative to the other physiological measuresto determine a pathophysiology indicating an absence, an onset, aprogression, a regression, and a status quo of diseases other thancongestive heart failure.
 24. A system according to claim 22, furthercomprising: the server system receiving a set of one or more quality oflife measures relating to patient information recorded on asubstantially continuous basis or derived therefrom; the database modulestoring the quality of life measures set into the patient care recordfor the individual patient into the database; and the analyzer analyzingthe quality of life measures in the physiological measures set relativeto the other quality of life measures to determine a pathophysiologyindicating an absence, an onset, a progression, a regression, and astatus quo of congestive heart failure.
 25. A system according to claim22, further comprising: the server system receiving a set of one or morebaseline physiological measures relating to patient information recordedduring an initial time period or derived therefrom; the database modulestoring the baseline physiological measures set into the patient carerecord for the individual patient into the database; and the analyzeranalyzing the physiological measures in the physiological measures setrelative to the baseline physiological measures to determine apathophysiology indicating an absence, an onset, a progression, aregression, and a status quo of congestive heart failure.
 26. A systemaccording to claim 22, further comprising: a comparison moduleretrieving the other physiological measures from measures sets for atleast one of an individual patient, a peer group, and a overall patientpopulation.
 27. A method for analyzing a patient status for congestiveheart failure for use in automated patient care, comprising: receiving aset of one or more physiological measures relating to patientinformation recorded on a substantially continuous basis or derivedtherefrom; storing the physiological measures set into a patient carerecord for an individual patient into a database; and analyzing one ormore of the physiological measures in the physiological measures setrelative to one or more other physiological measures to determine apathophysiology indicating an absence, an onset, a progression, aregression, and a status quo of congestive heart failure.
 28. A methodaccording to claim 27, further comprising: analyzing the physiologicalmeasures in the physiological measures set relative to the otherphysiological measures to determine a pathophysiology indicating anabsence, an onset, a progression, a regression, and a status quo ofdiseases other than congestive heart failure.
 29. A method according toclaim 27, further comprising: receiving a set of one or more quality oflife measures relating to patient information recorded on asubstantially continuous basis or derived therefrom; storing the qualityof life measures set into the patient care record for the individualpatient into the database; and analyzing the quality of life measures inthe physiological measures set relative to the other quality of lifemeasures to determine a pathophysiology indicating an absence, an onset,a progression, a regression, and a status quo of congestive heartfailure.
 30. A method according to claim 27, further comprising:receiving a set of one or more baseline physiological measures relatingto patient information recorded during an initial time period or derivedtherefrom; storing the baseline physiological measures set into thepatient care record for the individual patient into the database; andanalyzing the physiological measures in the physiological measures setrelative to the baseline physiological measures to determine apathophysiology indicating an absence, an onset, a progression, aregression, and a status quo of congestive heart failure.
 31. A methodaccording to claim 27, further comprising: retrieving the otherphysiological measures from measures sets for at least one of anindividual patient, a peer group, and a overall patient population. 32.A computer-readable storage medium for a device holding code forperforming the method according to claims 27, 28, 29, 30, or 31.